Can the optimal value of discount factor in Deep Reinforcement Learning be between 0.2 to 0.8?
The discount factor is not something you should be optimising. It is typically part of the problem statement.

For practical purposes, you may set it below 1.0 for continuous problems when in fact you care about best long-term reward. Another option to avoid infinities on continuous problems is to re-formulate the problem as optimising average reward. A high discount factor of e.g. 0.99 or 0.999 should produce a similar policy as one based on average reward.


  Is the author correct here or does it depend on cases? 


The author appears to be either completely wrong, or just poor at explaining themselves on this part. 


  If it is, then what kind of problems and/or situations should I set the discount factor as low as such figure at?


A low discount factor is for when you care much more about immediate rewards. You set it that low when that is the case. You decide what you care about when you set the learning problem. The value of the discount factor is part of the setup that decides what the optimal policy is. You never set it low "to help with optimising" because changing the value could change the optimal policy.